Syl Guardian MCP Server

Get a pre-signed URL for uploading a large CSV (more than ~200 rows). Use this instead of pasting rows into ``guardian_review``. Returns ``upload_url``, ``dataset_id`` and a ready-to-run ``curl`` command: run it (replace ``data.csv`` with your file; gzip is accepted with ``Content-Encoding: gzip``), then call ``guardian_review`` with ``dataset_id`` (and no ``csv``/``data``). The URL is single-use and expires after 30 minutes; the dataset is only usable by the same API key, and is single-use too: it is deleted when ``guardian_review`` reads it (even if that review fails), so request a new upload to retry. This call does not consume review quota.

Sanity-check a causal claim ("cause drove effect") before acting on it. Provide exactly one of ``csv`` (CSV text), ``data`` (list of row objects) or ``dataset_id`` (a dataset uploaded via ``guardian_request_upload``; use this for more than ~200 rows; single-use, deleted when reviewed) containing columns named by ``cause`` and ``effect``. Returns a verdict -- ``pass`` (act), ``caution`` (act carefully), or ``hold`` (do not act; the effect looks confounded or unsupported) -- with the naive correlational effect versus the confounder-adjusted causal effect and its confidence interval, the confounders adjusted for, a confidence score, a plain-English interpretation, and a recommended action. All numbers are deterministic statistics (never LLM-generated); only the interpretation prose may be AI-written. Args: cause: Treatment column the claim says drives the outcome (e.g. "ad_spend"). effect: Outcome column (e.g. "new_customers"). csv: Dataset as CSV text (header row required). data: Dataset as a list of row records (alternative to ``csv``). dataset_id: ID returned by ``guardian_request_upload`` after uploading the CSV (alternative to ``csv``/``data``; for datasets above ~200 rows). Single-use: the dataset is deleted when reviewed, so re-upload to retry. known_confounders: Columns to adjust for; otherwise discovered automatically. context: Free-text business context, e.g. "Q3 promo evaluation". units: Display-only unit hints by column, e.g. {"ad_spend": "USD"}. step: A concrete treatment step (e.g. 1000 for a $1,000 spend increase) to restate the effect in business terms (cost per incremental outcome, ...). time_col: Column to order rows by for time series (autocorrelation-robust standard errors and adstock). adstock: Opt-in geometric-adstock transform for a lagged/carryover treatment. se: Standard-error flavor; "auto" diagnoses autocorrelation when ``time_col`` is given. test_design: ``true`` (or an options object) also returns how many periods a spend-cut test needs to confirm the effect.